Related
My current df looks like the following:
WEEK COUNT COUNT2 PERCENTAGE
2017-53 10 15 .05
2018-00 5 10 .1
2018-01 7 9 .1
....
2018-52 10 12 .06
2019-00 6 10 .05
....
What I would like to do is combine the last two weeks of each year together into the final week of the year and combine COUNT, COUNT2, and PERCENTAGE. The weeks I currently have that I would like to combine are: 2017-53 and 2018-00, 2018-52 and 2019-00, 2019-52 and 2020-00. Which I would like to merge into 2017-53, 2018-52, 2019-52 My expected output would be the following:
WEEK COUNT COUNT2 PERCENTAGE
2017-53 15 25 .15
2018-01 7 9 .1
....
2018-52 16 22 .11
....
With tidyverse, after converting the 'WEEK' to Date class, arrange by that column, extract the 'year', create a grouping with 'WEEK' based on the difference of adjacent elements of 'year', and then summarise to get the sum of the columns that matches 'COUNT' or 'PERCENTAGE'
library(stringr)
library(lubridate)
library(dplyr) #1.0.0
df1 %>%
mutate(Date = as.Date(str_c(WEEK, "-01"), format = '%Y-%U-%w')) %>%
arrange(Date) %>%
mutate(year = year(Date)) %>%
group_by(WEEK = case_when(lag(year, default = first(year)) - year < 0 ~
lag(WEEK), TRUE ~ WEEK)) %>%
summarise(across(matches("COUNT|PERCENTAGE"), sum))
# A tibble: 3 x 4
# WEEK COUNT COUNT2 PERCENTAGE
# <chr> <int> <int> <dbl>
#1 2017-53 15 25 0.15
#2 2018-01 7 9 0.1
#3 2018-52 16 22 0.11
data
df1 <- structure(list(WEEK = c("2017-53", "2018-00", "2018-01", "2018-52",
"2019-00"), COUNT = c(10L, 5L, 7L, 10L, 6L), COUNT2 = c(15L,
10L, 9L, 12L, 10L), PERCENTAGE = c(0.05, 0.1, 0.1, 0.06, 0.05
)), class = "data.frame", row.names = c(NA, -5L))
You could use colSums() as is shown here, but it's a bit convoluted. I'd recommend using aggregate and pipes, as is shown further down in the same link.
Hope this helps!
I have a data frame in R which looks like below
Model Month Demand Inventory
A Jan 10 20
B Feb 30 40
A Feb 40 60
I want the data frame to look
Jan Feb
A_Demand 10 40
A_Inventory 20 60
A_coverage
B_Demand 30
B_Inventory 40
B_coverage
A_coverage and B_Coverage will be calculated in excel using a formula. But the problem I need help with is to pivot the data frame from wide to long format (original format).
I tried to implement the solution from the linked duplicate but I am still having difficulty:
HD_dcast <- reshape(data,idvar = c("Model","Inventory","Demand"),
timevar = "Month", direction = "wide")
Here is a dput of my data:
data <- structure(list(Model = c("A", "B", "A"), Month = c("Jan", "Feb",
"Feb"), Demand = c(10L, 30L, 40L), Inventory = c(20L, 40L, 60L
)), class = "data.frame", row.names = c(NA, -3L))
Thanks
Here's an approach with dplyr and tidyr, two popular R packages for data manipulation:
library(dplyr)
library(tidyr)
data %>%
mutate(coverage = NA_real_) %>%
pivot_longer(-c(Model,Month), names_to = "Variable") %>%
pivot_wider(id_cols = c(Model, Variable), names_from = Month ) %>%
unite(Variable, c(Model,Variable), sep = "_")
## A tibble: 6 x 3
# Variable Jan Feb
# <chr> <dbl> <dbl>
#1 A_Demand 10 40
#2 A_Inventory 20 60
#3 A_coverage NA NA
#4 B_Demand NA 30
#5 B_Inventory NA 40
#6 B_coverage NA NA
I have two data frames looking like that
data frame 1:
P.X value
OOPA 5
POKA 4
JKIO 3
KOPP 1
data frame 2:
P.X.1 P.X.2 P.X.3 P.X.4 mass
JKIO UIX HOP 56
CX OOPA 44
EDD POKA 13
KOPP FOSI 11
and I want to merge the two data files based on the df1 P.X and df2 P.X.1,P.X.2,P.X.3,P.X.4. So if it the JKIO in P.X.2. appears in the P.X one then merge them in a new data frame in the same row JKIO, 3, 56 as below:
data frame new:
P.X value mass
OOPA 5 44
POKA 4 13
JKIO 3 56
KOPP 1 11
Do you know how can I do it maybe with
merge(df1,df2 by(P.X == P.X.1 | P.X.2 | P.X.3 | P.X.4)
?
The following is one way to achieve your goal. You want to convert df2 to a long-format data and get rows that have more than 1 character. Once you have this data, you merge df1 with the updated df2.
library(dplyr)
library(tidyr)
left_join(df1,
pivot_longer(df2, cols = P.X.1:P.X.4, names_to = "foo",
values_to = "P.X") %>% filter(nchar(P.X) > 0),
by = "P.X") %>%
select(-foo)
P.X value mass
1 OOPA 5 44
2 POKA 4 13
3 JKIO 3 56
4 KOPP 1 11
DATA
df1 <- structure(list(P.X = c("OOPA", "POKA", "JKIO", "KOPP"), value = c(5L,
4L, 3L, 1L)), class = "data.frame", row.names = c(NA, -4L))
df2 <- structure(list(P.X.1 = c("", "", "EDD", "KOPP"), P.X.2 = c("JKIO",
"", "", "FOSI"), P.X.3 = c("UIX", "CX", "POKA", ""), P.X.4 = c("HOP",
"OOPA", "", ""), mass = c(56, 44, 13, 11)), row.names = c(NA,
-4L), class = c("tbl_df", "tbl", "data.frame"))
You could also just do:
df_new <- cbind(df1, df2[,5])
The data frame is like this:
enter image description here
header: system
Row 1: 00000000000000000503_0
Row 2: 00000000000000000503_1
Row 3: 00000000000000000503_2
Row 4: 00000000000000000503_3
Row 5: 000000000000000004e7_0
Row 6: 000000000000000004e7_1
Row 7: 00000000000000000681_0
Row 8: 00000000000000000681_1
Row 9: 00000000000000000681_2
I want to generate a frequency table with the quantities of the code before string "_" such that:
"00000000000000000503" appears 4 times, "000000000000000004e7" appears 2 times, and so on.
How do I do this in R?
Remove everything after underscore and use table to count frequency
table(sub("_.*", "", data$col1))
#Also
#table(sub("(.*)_.*", "\\1", data$col1))
#000000000000000004e7 00000000000000000503 00000000000000000681
# 2 4 3
If final output needs to be a dataframe use stack
stack(table(sub("_.*", "", data$col1)))
# values ind
#1 2 000000000000000004e7
#2 4 00000000000000000503
#3 3 00000000000000000681
data
data <- structure(list(col1 = structure(c(3L, 4L, 5L, 6L, 1L, 2L, 7L,
8L, 9L), .Label = c("000000000000000004e7_0", "000000000000000004e7_1",
"00000000000000000503_0", "00000000000000000503_1",
"00000000000000000503_2",
"00000000000000000503_3", "00000000000000000681_0",
"00000000000000000681_1",
"00000000000000000681_2"), class = "factor")), class = "data.frame",
row.names = c(NA, -9L))
A dplyr-tidyr alternative:
df %>%
tidyr::separate(V3, c("target", "non_target")) %>%
count(target)
# A tibble: 3 x 2
target n
<chr> <int>
1 000000000000000004e7 2
2 00000000000000000503 4
3 00000000000000000681 3
With base:
table(sapply(strsplit(df$system, "_"),"[[", 1))
Data:
df <- structure(list(V1 = c("Row", "Row", "Row", "Row", "Row", "Row",
"Row", "Row", "Row"), V2 = c("1:", "2:", "3:", "4:", "5:", "6:",
"7:", "8:", "9:"), V3 = c("00000000000000000503_0", "00000000000000000503_1",
"00000000000000000503_2", "00000000000000000503_3", "000000000000000004e7_0",
"000000000000000004e7_1", "00000000000000000681_0", "00000000000000000681_1",
"00000000000000000681_2")), class = "data.frame", row.names = c(NA,
-9L))
Another option using the stringr library that is included in tidyverse
> library(tidyverse)
> mydata <- data.frame(system = c("00000000000000000503_0",
"00000000000000000503_1",
"00000000000000000503_2",
"00000000000000000503_3",
"000000000000000004e7_0",
"000000000000000004e7_1",
"00000000000000000681_0",
"00000000000000000681_1",
"00000000000000000681_2"))
> mydata
system
1 00000000000000000503_0
2 00000000000000000503_1
3 00000000000000000503_2
4 00000000000000000503_3
5 000000000000000004e7_0
6 000000000000000004e7_1
7 00000000000000000681_0
8 00000000000000000681_1
9 00000000000000000681_2
> # Split data using str_split
> mydata$leftside <- sapply(mydata$system, function(x) unlist(str_split(x, "_"))[1]) #split string by the "_" and take first piece
> mydata$rightside <- sapply(mydata$system, function(x) unlist(str_split(x, "_"))[2]) #split string by the "_" and take second piece
>
> mydata
system leftside rightside
1 00000000000000000503_0 00000000000000000503 0
2 00000000000000000503_1 00000000000000000503 1
3 00000000000000000503_2 00000000000000000503 2
4 00000000000000000503_3 00000000000000000503 3
5 000000000000000004e7_0 000000000000000004e7 0
6 000000000000000004e7_1 000000000000000004e7 1
7 00000000000000000681_0 00000000000000000681 0
8 00000000000000000681_1 00000000000000000681 1
9 00000000000000000681_2 00000000000000000681 2
> # alternative tabulate fuction than base::table(). Can Provide nicer options.
> xtabs(data = mydata, formula = ~leftside)
leftside
000000000000000004e7 00000000000000000503 00000000000000000681
2 4 3
A tidyverse answer would be
my_data <- mydata %>%
mutate_if(is.factor, as.character) %>%
mutate(system = gsub('_[^_]*$', '', system)) %>%
group_by(system) %>%
count() %>%
ungroup()
my_data
An option with str_remove and group_by
library(stringr)
library(dplyr)
df %>%
group_by(V3 = str_remove(V3, "_\\d+$")) %>%
summarise(n = n())
# A tibble: 3 x 2
# V3 n
# <chr> <int>
#1 000000000000000004e7 2
#2 00000000000000000503 4
#3 00000000000000000681 3
Or in base R with table and trimws
table(trimws(df$V3, whitespace = "_[0-9]+"))
data
df <- structure(list(V1 = c("Row", "Row", "Row", "Row", "Row", "Row",
"Row", "Row", "Row"), V2 = c("1:", "2:", "3:", "4:", "5:", "6:",
"7:", "8:", "9:"), V3 = c("00000000000000000503_0", "00000000000000000503_1",
"00000000000000000503_2", "00000000000000000503_3", "000000000000000004e7_0",
"000000000000000004e7_1", "00000000000000000681_0", "00000000000000000681_1",
"00000000000000000681_2")), class = "data.frame", row.names = c(NA,
-9L))
I have been attempting to sort my dataframe by the first column - or day - with multiple different methods listed below to no avail. I suspect it could be because it is attempting to order by the first number but I am unsure how I would change that to get it to order the rows properly. The dataset is as follows:
df1
[day][sample1][sample2]
[1,]day0 22 11
[2,]day11 23 15
[3,]day15 25 14
[4,]day2 21 13
[5,]day8 20 17
...
I am looking to order the entire row by day. I have tried the following
df[sort(as.character(df$day)),]
df[order(as.character(df$day)),]
mixedorder(as.character(df$day)) (gtools package)
The mixedorder merely output an index of numbers.
Current Code:
df_0$day = metadata_df[,3]
df_0 <- df_0[,c(8,1:7)]
df1 <- aggregate(df_0[,2:ncol(df_0)], df_0[1], mean)
df1 <- df1[mixedorder(as.character(df1$day)),]
df1$day <- factor(df1$day, levels = unique(df1$day))
rownames(df1) <- 1:nrow(df1)
##Plotting expression levels
Plot1 <- ggplot() +
geom_line(data=df1, aes(x=day, y=sample1, group=1, color="blue"))+
geom_line(data=df2, aes(x=day, y=sample1, group=2, color="red"))
Note that I have done the same transformations with df2 as I have with df1. Both df1 and df2 are the same, except with slightly different values in them.
The mixedorder gives the ordered index which can be used to order the rows
df1 <- df[mixedorder(as.character(df$day)),]
df1
# day sample1 sample2
#1 day0 22 11
#4 day2 21 13
#5 day8 20 17
#2 day11 23 15
#3 day15 25 14
It is not clear about how the OP is plotting.
library(tidyverse)
df1 %>%
mutate(day = factor(day, levels = unique(day))) %>%
gather(key, val, -day) %>%
ggplot(., aes(x = day, y = val, color = key)) +
geom_point()
data
df <- structure(list(day = structure(1:5, .Label = c("day0", "day11",
"day15", "day2", "day8"), class = "factor"), sample1 = c(22L,
23L, 25L, 21L, 20L), sample2 = c(11L, 15L, 14L, 13L, 17L)), .Names = c("day",
"sample1", "sample2"), class = "data.frame", row.names = c(NA,
-5L))